When bilingualism isn't enough: perspectives of new speakers of French on multilingualism in Montreal
Bibliographic record
Abstract
Montreal, the largest city in the province of Quebec, Canada, is where most newcomers settle down. Many will attend one of the ‘francization’ (French as a second language) courses offered by the provincial government. Learning French and its adoption as a common language are essential conditions to gain social inclusion through participation in public life and the labour market. However, Montreal is by no means a monolingual city with about a third of the population having a language other than French as their first language. Research shows a clear trend toward French/English bilingual elitism [Lamarre et al. 2015. La socialisation langagière comme processus dynamique : suivi d'une cohorte de jeunes plurilingues intégrant le marché du travail. Québec, QC: Conseil supérieur de la langue française] and towards plurilingual elitism. This ethnographic study investigates the experience of newcomers who attend the ‘francization’ programme as new speakers of French [O'Rourke, Pujolar, and Ramallo 2015. “New speakers of minority languages: the challenging opportunity - Foreword.” International Journal of the Sociology of Language 2015 (231): 1–20. doi:10.1515/ijsl-2014-0029]. It analyses the use of their linguistic resources to access eliteness and social inclusion. In a context where public discourse strongly promotes a monolingual ideology, the plurilingual repertoires of newcomers are not always recognised as a valuable resource. However, newcomers’ language practices show that their plurilingual repertoire has symbolic and material value beyond the elite French/English bilingualism, thus challenging the boundaries between elite and non-elite linguistic groups in Montreal.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.037 | 0.020 |
| Scholarly communication | 0.011 | 0.004 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".